Cross-lingual approaches to sentiment analysis attempt to leverage available sentiment annotations in a high-resource language for target languages which lack annotated data. This is especially important when the cost of annotating a high-quality sentiment dataset, such as the Stanford Sentiment Treebank [1]}, can be prohibitive (215,154 phrases, each annotated by 3 annotators, at 10 cents an annotation would be 64,546€!). Therefore, it is preferable to make use of those datasets that already exist. Although most approaches to cross-lingual sentiment analysis rely on Machine Translation [2]}, [3]}, [4]}, this requires large amounts of parallel data,
making it less helpful for under-resourced languages.
